Modeling and optimization of the corrosion resistance of Cr-free and Cr-based chemical conversion coatings on nickel foil by artificial neural network and response surface method

Chemical conversion treatment is one of the most powerful methods to prevent the corrosion of metals. However, determining the optimal addition amount of various active ingredients for the enhancement of corrosion resistance requires a large number of repeated experiments, which is very time-consuming and is also not helpful for figuring out the interactions between active ingredients. In the present study, Cr-free conversion coatings (CFCC) and Cr-based conversion coatings (CCC) were prepared on nickel foil and characterized by scanning electron microscopy and X-ray photoelectron spectroscopy to determine their morphology and composition of them. The corrosion resistance of conversion coatings was measured by salt spray test and further investigated using the response surface method (RSM) and artificial neural network (ANN) approach. The constructed models displayed high predictive accuracy, and the coefficient of correlation value of the RSM-CFCC, ANN-CFCC, RSM-CCC, and ANN-CCC models is 0.9441, 0.9513, 0.9535 and 0.9564, respectively. This comparative study recommends the ANN model best fits the experimental data for both CFCC and CCC. By using the ANN outputs, satisfactory results can be estimated with superior accuracy and efficiency while the RSM model can describe the interactions between factors.